Fair Classification

Fair Classification is a methodological framework in machine learning that integrates fairness constraints or criteria into the classification process to mitigate discriminatory outcomes against sensitive attributes while maintaining predictive utility.

229

Publications

20.2K

Citations

667

Authors

238

Institutions

Publications per year

2017–2026

206

Authors

667

  1. KP
    Krishna P. Gummadi

    Max Planck Institute for Software Systems, Germany

    10 publications · 2.1K citations

    10

    h-index

  2. IV
    Isabel Valera

    Max Planck Institute for Software Systems, Germany

    9 publications · 2.1K citations

    9

    h-index

  3. XW
    Xintao Wu

    University of Arkansas at Fayetteville, United States

    8 publications · 603 citations

    8

    h-index

  4. JK
    Jon Kleinberg

    Cornell University, United States

    7 publications · 1.5K citations

    7

    h-index

  5. CD
    Cynthia Dwork

    IBM Research - Almaden, United States

    7 publications · 4.5K citations

    7

    h-index

1–5 of 667

Venues

  1. arXiv (Cornell University)

    Repository · 68 publications · 4.6K citations

    27

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 517 citations

    13

    h-index

  3. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

    Repository · 5 publications · 781 citations

    5

    h-index

  4. MPG.PuRe (Max Planck Society)

    Repository · 4 publications · 427 citations

    4

    h-index

  5. 2022 ACM Conference on Fairness, Accountability, and Transparency

    Conference · 3 publications · 94 citations

    3

    h-index

1–5